Automatic generation of multiple pronunciations based on neural networks

Fukada, Toshiaki; Yoshimura, Takeshi; Sagisaka, Yoshinori

doi:10.1016/s0167-6393(98)00066-1

Cited by 34 publications

(21 citation statements)

References 14 publications

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“…On the other hand, an alternative pronunciation dictionary uses the actual phone (or other sub-word) sequences pronounced in speech. In an alternative pronunciation dictionary, various pronunciation variations can be included (Fukada et al 1999).…”

Section: The Pronunciation Dictionarymentioning

confidence: 99%

Syllable-based speech recognition for Amharic

Abate¹,

Menzel²

2007

Proceedings of the 2007 Workshop on Computational Approaches to Semitic Languages Common Issues and Resources - Semitic '07

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Amharic is the Semitic language that has the second large number of speakers after Arabic (Hayward and Richard 1999). Its writing system is syllabic with Consonant-Vowel (CV) syllable structure. Amharic orthography has more or less a one to one correspondence with syllabic sounds. We have used this feature of Amharic to develop a CV syllable-based speech recognizer, using Hidden Markov Modeling (HMM), and achieved 90.43% word recognition accuracy.

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Section: The Pronunciation Dictionarymentioning

confidence: 99%

Syllable-based speech recognition for Amharic

Abate¹,

Menzel²

2007

Proceedings of the 2007 Workshop on Computational Approaches to Semitic Languages Common Issues and Resources - Semitic '07

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“…A possible way to introduce pronunciation variants into TTS is to manually add alternative pronunciations directly into the dictionary [26]. Rule-based pronunciation lattices have also been used to reduce inconsistencies between the phoneme sequence generated by the front-end text processing system (i.e.…”

Section: Studies On Pronunciation Variants Modellingmentioning

confidence: 99%

Can We Generate Emotional Pronunciations for Expressive Speech Synthesis?

Tahon

Lecorvé

Lolive

2020

IEEE Trans. Affective Comput.

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Abstract-In the field of expressive speech synthesis, a lot of work has been conducted on suprasegmental prosodic features while few has been done on pronunciation variants. However, prosody is highly related to the sequence of phonemes to be expressed. This article raises two issues in the generation of emotional pronunciations for TTS systems. The first issue consists in designing an automatic pronunciation generation method from text, while the second issue addresses the very existence of emotional pronunciations through experiments conducted on emotional speech. To do so, an innovative pronunciation adaptation method which automatically adapts canonical phonemes first to those labeled in the corpus used to create a synthetic voice, then to those labeled in an expressive corpus, is presented. This method consists in training conditional random fields pronunciation models with prosodic, linguistic, phonological and articulatory features. The analysis of emotional pronunciations reveals strong dependencies between prosody and phoneme assimilation or elisions. According to perceptual tests, the double adaptation allows to synthesize expressive speech samples of good quality, but emotion-specific pronunciations are too subtle to be perceived by testers.

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“…A significant improvement in recognition accuracy was obtained on (RM) task using the pronunciation networks thus derived, relative to the base form pronunciations. In Fukada et al (1999), authors described the procedure for generating the phoneme sequence automatically using their general purpose phonetic front end. In order to generate a pronunciation string for each word, a neural network first assigned a score fro each of 39 phonemes to each 6 msec frame in the word.…”

Section: Automatic Generation Of a Pronunciation Dictionarymentioning

confidence: 99%

“…In Fukada et al (1999), authors stated that several approaches have been adopted over the years for grapheme-to-phone conversion for European Portuguese: hand derived rules, neural networks, classification and regression trees, etc. their first approach to grapheme-to-phone conversion was a rule-based system with about 200 rules.…”

Section: Automatic Generation Of a Pronunciation Dictionarymentioning

confidence: 99%

Arabic speech recognition using SPHINX engine

Hyassat

Zitar

2006

Int J Speech Technol

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Although the Arab world has an estimated number of 250 million Arabic speakers, there has been little research on Arabic speech recognition when compared to other languages of similar importance (e.g. Mandarin). Due to the lack of diacritic Arabic text and the lack of Pronunciation Dictionary (PD), most of previous work on Arabic Automatic Speech Recognition has been concentrated on developing recognizers using Romanized characters i.e. let the system recognizes the Arabic word as an English one, then map it to Arabic word from lookup table that maps the Arabic word to its Romanized pronunciation.In this work, we introduce the first SPHINX-IV-based Arabic recognizer and propose an automatic toolkit, which is capable of producing (PD) for both Holly Qura'an and standard Arabic language. Three corpuses are completely developed in this work, namely the Holly Qura'an Corpus HQC-1 about 18.5 hours, the command and control corpus CAC-1 about 1.5 hours and Arabic digits corpus ADC less than one hour of speech. The building process is completely described. Fully diacritic Arabic transcriptions, for all the three corpuses were developed too.SPHINX-IV engine was customized and trained, for both the language model and the lexicon modules shown in the frame work architecture block diagram on next page.Using the three mentioned corpuses; the (PD) developed by our automatic tool with the transcripts, SPHINX-IV engine is trained and tuned in order to develop three acoustic models, one for each corpus. Training is based on an HMM model that is built on statistical information and random variables distributions extracted from the training data itself. New algorithm is proposed to add unlabeled data to the training corpus in order to increase the corpus size. This algorithm is based on Neural Network confidence scorer and then is used to annotate the decoded speech in order to decide whether the proposed transcript is accepted and can be added to the seed corpus or not.The model parameters were fine-tuned using simulated annealing algorithm; optimum values were tested and reported. Our major contribution is mainly using the open source SPHINX-IV model in Arabic speech recognition by building our own language and acoustic models without Romanization for the Arabic speech. The system is fine-tuned and data are refined for training and validation. Optimum values for number of Gaussian mixtures distributions and number of states in HMM's have been found according to specified performance measures. Optimum values for confidence 134 Int J Speech Technol (2006) 9: 133-150 scores were found for the training data. Although much more work need to be done to complete the work with this size, we consider the corpus used in our system is enough to validate our approach. SPHINX has never been used before in this manner for Arabic speech recognition. The work is an invitation for all open source speech recognition developers and groups to take over and capitalize on what we have started.

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Automatic generation of multiple pronunciations based on neural networks

Cited by 34 publications

References 14 publications

Syllable-based speech recognition for Amharic

Syllable-based speech recognition for Amharic

Can We Generate Emotional Pronunciations for Expressive Speech Synthesis?

Arabic speech recognition using SPHINX engine

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